Research On Key Technologies Of Computer Aided Diagnosis For Early-stage Lung Cancer In Thoracic CT Images

Lung cancer is the most common cause of cancer death for both men and women around the whole world. Clinical studies show that the stage of the disease is the most important factor in preventing and prognosing lung cancer. Early detection and surgical resection of malignant lung nodules (T1) can improve the prognosis significantly. Computer Aided Diagnosis (CAD) methods can assist radiologists in reducing the reading time as well as in improving the diagnostic accuracy. Usually, CAD schemes for lung cancer consist of preprocessing, candidate detection, false positive (FP) reduction and classification. The preprocessing stage restricts the search space to the lung regions and reduces noise and image artifacts. To improve the sensitivity of candidate detection, a nodule enhancement filter can be applied as a preprocessing step prior to the initial nodule detection.First, we developed one enhancement filter to enhance the blob like nodules and suppress the curvilinear structures like vessels which are one of the primary sources for false positives. In the research, the dissertation proposed an enhancement filter to enhance the blob like nodules based on Morphological Component Analysis (MCA) theory, which can represent isotropic and anisotropic features as sparse combinations of atoms of predetermined dictionaries. According to its results, the enhancement can be achieved by separating and analyziong objects with different shapes.Second, we developed another enhancement filter through employing the shift-invariant undecimated wavelet transform and analyzing the eigenvalues of the Hessian matrix. In contrast with the results obtained when using the blob enhancement filter on 2D slices, most crossings and end points of blood vessels as FPs have been substantially eliminated, by means of analyzing the connectivity between their context slices with MPR techniques.Meanwhile, we investigated how to exploit multiple medical image features to limit the search space to the regions of interests.Besides medical image enhancement, the dissertation also worked on medical image segmentation and analysis. Segmentation is often a necessary first step to computer analysis. This dissertation proposed a segmentation framework to extract the volumes of interest (VOIs) using optical flow constraint refined with Sobolev gradient. In this work, to avoid using normal gradient that could be sensitive to noise and make the flow converge to local minima, optical flow method was exploited in Sobolev space instead of Euclidean space.The dissertation proposed a segmentation framework on analysis of blood vessels in thoracic CT scans. Determination and assessment of vessel morphology and topology is crucial for diagnosing, treatment, outcome prediction, and surgical planning. The dissertation presented a new level set functional combined with both region-based models (e.g. CV) and edge-based models (e.g. GAC) through mutual information sharing based on game theory.Finally, the dissertation presented a method to recognize the lung nodules based on undecimated wavelet and watershed transforms, since doubling time of pulmonary nodules is important for estimating malignancy versus benignity. The thoracic CT images are enhanced using translation invariant redundant wavelet transform. The lung nodules can be segmented by exploiting watershed in enhanced volume. Experiments on the thoracic CT images showed that the proposed method obtained satisfactory results of nodule recognition.